RcppDE
Global optimization by differential evolution in C++

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DEoptim-methods: DEoptim-methods

DEoptim-methods: DEoptim-methods
In RcppDE: Global optimization by differential evolution in C++

Description Usage Arguments Details Note Author(s) References See Also Examples

Description

Methods for DEoptim objects.

Usage

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## S3 method for class 'DEoptim'
summary(object, ...)
## S3 method for class 'DEoptim'
plot(x, plot.type = c("bestmemit", "bestvalit", "storepop"), ...)

Arguments

object

an object of class DEoptim; usually, a result of a call to DEoptim.

x

an object of class DEoptim; usually, a result of a call to DEoptim.

plot.type

should we plot the best member at each iteration, the best value at each iteration or the intermediate populations?

...

further arguments passed to or from other methods.

Details

Members of the class DEoptim have a plot method that accepts the argument plot.type. plot.type = "bestmemit" results in a plot of the parameter values that represent the lowest value of the objective function each generation. plot.type = "bestvalit" plots the best value of the objective function each generation. Finally, plot.type = "storepop" results in a plot of stored populations (which are only available if these have been saved by setting the control argument of DEoptim appropriately). Storing intermediate populations allows us to examine the progress of the optimization in detail. A summary method also exists and returns the best parameter vector, the best value of the objective function, the number of generations optimization ran, and the number of times the objective function was evaluated.

Note

Further details and examples of the R package DEoptim can be found in Mullen et al. (2009) and Ardia et al. (2010).

Please cite the package in publications.

Author(s)

For RcppDE: Dirk Eddelbuettel.

For DEoptim: David Ardia, Katharine Mullen katharine.mullen@nist.gov, Brian Peterson and Joshua Ulrich.

References

Mullen, K.M., Ardia, D., Gil, D.L, Windover, D., Cline, J. (2009) DEoptim: An R Package for Global Optimization by Differential Evolution. URL http://ssrn.com/abstract=1526466

Ardia, D., Boudt, K., Carl, P., Mullen, K.M., Peterson, B.G. (2010) Differential Evolution (DEoptim) for Non-Convex Portfolio Optimization. URL http://ssrn.com/abstract=1584905

See Also

DEoptim and DEoptim.control.

Examples

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  ## Rosenbrock Banana function
  ## The function has a global minimum f(x) = 0 at the point (0,0).  
  ## Note that the vector of parameters to be optimized must be the first 
  ## argument of the objective function passed to DEoptim.
  Rosenbrock <- function(x){
    x1 <- x[1]
    x2 <- x[2]
    100 * (x2 - x1 * x1)^2 + (1 - x1)^2
  }

  lower <- c(-10, -10)
  upper <- -lower
  
  set.seed(1234)
  outDEoptim <- DEoptim(Rosenbrock, lower, upper)
  
  ## print output information
  summary(outDEoptim)

  ## plot the best members
  plot(outDEoptim, type = 'b')

  ## plot the best values
  dev.new()
  plot(outDEoptim, plot.type = "bestvalit", type = 'b', col = 'blue')

  ## rerun the optimization, and store intermediate populations
  outDEoptim <- DEoptim(Rosenbrock, lower, upper,
                        DEoptim.control(itermax = 500,
                        storepopfrom = 1, storepopfreq = 2))
  summary(outDEoptim)
  
  ## plot intermediate populations
  dev.new()
  plot(outDEoptim, plot.type = "storepop")

  ## Wild function
  Wild <- function(x)
    10 * sin(0.3 * x) * sin(1.3 * x^2) +
       0.00001 * x^4 + 0.2 * x + 80

  outDEoptim = DEoptim(Wild, lower = -50, upper = 50,
                       DEoptim.control(trace = FALSE, storepopfrom = 50,
                       storepopfreq = 1))
  
  plot(outDEoptim, type = 'b')

  dev.new()
  plot(outDEoptim, plot.type = "bestvalit", type = 'b')

## Not run: 
  ## an example with a normal mixture model: requires package mvtnorm
  library(mvtnorm)

  ## neg value of the density function
  negPdfMix <- function(x) {
     tmp <- 0.5 * dmvnorm(x, c(-3, -3)) + 0.5 * dmvnorm(x, c(3, 3))
     -tmp
  }

  ## wrapper plotting function
  plotNegPdfMix <- function(x1, x2)
     negPdfMix(cbind(x1, x2))

  ## contour plot of the mixture
  x1 <- x2 <- seq(from = -10.0, to = 10.0, by = 0.1)
  thexlim <- theylim <- range(x1)
  z <- outer(x1, x2, FUN = plotNegPdfMix)
  
  contour(x1, x2, z, nlevel = 20, las = 1, col = rainbow(20),
     xlim = thexlim, ylim = theylim)

  set.seed(1234)
  outDEoptim <- DEoptim(negPdfMix, c(-10, -10), c(10, 10),
     DEoptim.control(NP = 100, itermax = 100, storepopfrom = 1,
     storepopfreq = 5))

  ## convergence plot
  dev.new()
  plot(outDEoptim)
  
  ## the intermediate populations indicate the bi-modality of the function
  dev.new()
  plot(outDEoptim, plot.type = "storepop")

## End(Not run)

Example output

Now loading:
  RcppDE: C++ Implementation of Differential Evolution Optimisation
  Author: Dirk Eddelbuettel
Based on:
  DEoptim (version 2.0-7): Differential Evolution algorithm in R
  Authors: David Ardia, Katharine Mullen, Brian Peterson and Joshua Ulrich
Iteration: 1 bestvalit: 6.346930 bestmemit:   -1.516509    2.311679
Iteration: 2 bestvalit: 5.620014 bestmemit:    0.435505    0.419911
Iteration: 3 bestvalit: 2.735287 bestmemit:    1.069276    1.308593
Iteration: 4 bestvalit: 0.425506 bestmemit:    0.347692    0.120983
Iteration: 5 bestvalit: 0.425506 bestmemit:    0.347692    0.120983
Iteration: 6 bestvalit: 0.372295 bestmemit:    0.447835    0.174593
Iteration: 7 bestvalit: 0.372295 bestmemit:    0.447835    0.174593
Iteration: 8 bestvalit: 0.372295 bestmemit:    0.447835    0.174593
Iteration: 9 bestvalit: 0.372295 bestmemit:    0.447835    0.174593
Iteration: 10 bestvalit: 0.372295 bestmemit:    0.447835    0.174593
Iteration: 11 bestvalit: 0.372295 bestmemit:    0.447835    0.174593
Iteration: 12 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 13 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 14 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 15 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 16 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 17 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 18 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 19 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 20 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 21 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 22 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 23 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 24 bestvalit: 0.064301 bestmemit:    0.795540    0.647883
Iteration: 25 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 26 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 27 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 28 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 29 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 30 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 31 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 32 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 33 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 34 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 35 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 36 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 37 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 38 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 39 bestvalit: 0.002878 bestmemit:    1.022910    1.041495
Iteration: 40 bestvalit: 0.001137 bestmemit:    0.966398    0.934214
Iteration: 41 bestvalit: 0.001137 bestmemit:    0.966398    0.934214
Iteration: 42 bestvalit: 0.001137 bestmemit:    0.966398    0.934214
Iteration: 43 bestvalit: 0.001137 bestmemit:    0.966398    0.934214
Iteration: 44 bestvalit: 0.001137 bestmemit:    0.966398    0.934214
Iteration: 45 bestvalit: 0.001137 bestmemit:    0.966398    0.934214
Iteration: 46 bestvalit: 0.000225 bestmemit:    0.986703    0.972892
Iteration: 47 bestvalit: 0.000225 bestmemit:    0.986703    0.972892
Iteration: 48 bestvalit: 0.000225 bestmemit:    0.986703    0.972892
Iteration: 49 bestvalit: 0.000090 bestmemit:    0.992026    0.984634
Iteration: 50 bestvalit: 0.000090 bestmemit:    0.992026    0.984634
Iteration: 51 bestvalit: 0.000090 bestmemit:    0.992026    0.984634
Iteration: 52 bestvalit: 0.000090 bestmemit:    0.992026    0.984634
Iteration: 53 bestvalit: 0.000090 bestmemit:    0.992026    0.984634
Iteration: 54 bestvalit: 0.000090 bestmemit:    0.992026    0.984634
Iteration: 55 bestvalit: 0.000090 bestmemit:    0.992026    0.984634
Iteration: 56 bestvalit: 0.000090 bestmemit:    0.992026    0.984634
Iteration: 57 bestvalit: 0.000090 bestmemit:    0.992026    0.984634
Iteration: 58 bestvalit: 0.000090 bestmemit:    0.992026    0.984634
Iteration: 59 bestvalit: 0.000090 bestmemit:    0.992026    0.984634
Iteration: 60 bestvalit: 0.000050 bestmemit:    1.000960    1.002619
Iteration: 61 bestvalit: 0.000050 bestmemit:    1.000960    1.002619
Iteration: 62 bestvalit: 0.000050 bestmemit:    1.000960    1.002619
Iteration: 63 bestvalit: 0.000023 bestmemit:    1.004405    1.008635
Iteration: 64 bestvalit: 0.000023 bestmemit:    1.004405    1.008635
Iteration: 65 bestvalit: 0.000005 bestmemit:    1.002342    1.004696
Iteration: 66 bestvalit: 0.000005 bestmemit:    1.002342    1.004696
Iteration: 67 bestvalit: 0.000005 bestmemit:    1.002342    1.004696
Iteration: 68 bestvalit: 0.000003 bestmemit:    0.999158    0.998169
Iteration: 69 bestvalit: 0.000001 bestmemit:    1.000746    1.001415
Iteration: 70 bestvalit: 0.000001 bestmemit:    1.000746    1.001415
Iteration: 71 bestvalit: 0.000001 bestmemit:    1.000746    1.001415
Iteration: 72 bestvalit: 0.000001 bestmemit:    1.000746    1.001415
Iteration: 73 bestvalit: 0.000001 bestmemit:    1.000514    1.001100
Iteration: 74 bestvalit: 0.000000 bestmemit:    0.999758    0.999565
Iteration: 75 bestvalit: 0.000000 bestmemit:    0.999758    0.999565
Iteration: 76 bestvalit: 0.000000 bestmemit:    0.999758    0.999565
Iteration: 77 bestvalit: 0.000000 bestmemit:    0.999758    0.999565
Iteration: 78 bestvalit: 0.000000 bestmemit:    1.000336    1.000655
Iteration: 79 bestvalit: 0.000000 bestmemit:    1.000336    1.000655
Iteration: 80 bestvalit: 0.000000 bestmemit:    1.000336    1.000655
Iteration: 81 bestvalit: 0.000000 bestmemit:    1.000336    1.000655
Iteration: 82 bestvalit: 0.000000 bestmemit:    1.000336    1.000655
Iteration: 83 bestvalit: 0.000000 bestmemit:    1.000336    1.000655
Iteration: 84 bestvalit: 0.000000 bestmemit:    1.000336    1.000655
Iteration: 85 bestvalit: 0.000000 bestmemit:    0.999802    0.999621
Iteration: 86 bestvalit: 0.000000 bestmemit:    0.999802    0.999621
Iteration: 87 bestvalit: 0.000000 bestmemit:    0.999802    0.999621
Iteration: 88 bestvalit: 0.000000 bestmemit:    0.999802    0.999621
Iteration: 89 bestvalit: 0.000000 bestmemit:    0.999873    0.999752
Iteration: 90 bestvalit: 0.000000 bestmemit:    0.999873    0.999752
Iteration: 91 bestvalit: 0.000000 bestmemit:    0.999873    0.999752
Iteration: 92 bestvalit: 0.000000 bestmemit:    1.000029    1.000062
Iteration: 93 bestvalit: 0.000000 bestmemit:    1.000029    1.000062
Iteration: 94 bestvalit: 0.000000 bestmemit:    1.000029    1.000062
Iteration: 95 bestvalit: 0.000000 bestmemit:    1.000029    1.000062
Iteration: 96 bestvalit: 0.000000 bestmemit:    1.000029    1.000062
Iteration: 97 bestvalit: 0.000000 bestmemit:    1.000029    1.000062
Iteration: 98 bestvalit: 0.000000 bestmemit:    1.000029    1.000062
Iteration: 99 bestvalit: 0.000000 bestmemit:    1.000029    1.000062
Iteration: 100 bestvalit: 0.000000 bestmemit:    1.000029    1.000062
Iteration: 101 bestvalit: 0.000000 bestmemit:    1.000029    1.000062
Iteration: 102 bestvalit: 0.000000 bestmemit:    1.000029    1.000062
Iteration: 103 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 104 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 105 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 106 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 107 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 108 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 109 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 110 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 111 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 112 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 113 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 114 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 115 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 116 bestvalit: 0.000000 bestmemit:    1.000004    1.000008
Iteration: 117 bestvalit: 0.000000 bestmemit:    0.999996    0.999991
Iteration: 118 bestvalit: 0.000000 bestmemit:    0.999996    0.999991
Iteration: 119 bestvalit: 0.000000 bestmemit:    0.999996    0.999991
Iteration: 120 bestvalit: 0.000000 bestmemit:    1.000003    1.000005
Iteration: 121 bestvalit: 0.000000 bestmemit:    1.000003    1.000005
Iteration: 122 bestvalit: 0.000000 bestmemit:    1.000003    1.000005
Iteration: 123 bestvalit: 0.000000 bestmemit:    1.000003    1.000005
Iteration: 124 bestvalit: 0.000000 bestmemit:    1.000003    1.000005
Iteration: 125 bestvalit: 0.000000 bestmemit:    1.000003    1.000005
Iteration: 126 bestvalit: 0.000000 bestmemit:    1.000003    1.000005
Iteration: 127 bestvalit: 0.000000 bestmemit:    1.000001    1.000002
Iteration: 128 bestvalit: 0.000000 bestmemit:    1.000001    1.000002
Iteration: 129 bestvalit: 0.000000 bestmemit:    1.000001    1.000002
Iteration: 130 bestvalit: 0.000000 bestmemit:    1.000001    1.000003
Iteration: 131 bestvalit: 0.000000 bestmemit:    1.000000    0.999999
Iteration: 132 bestvalit: 0.000000 bestmemit:    1.000000    0.999999
Iteration: 133 bestvalit: 0.000000 bestmemit:    1.000000    0.999999
Iteration: 134 bestvalit: 0.000000 bestmemit:    1.000000    0.999999
Iteration: 135 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 136 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 137 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 138 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 139 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 140 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 141 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 142 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 143 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 144 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 145 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 146 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 147 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 148 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 149 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 150 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 151 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 152 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 153 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 154 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 155 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 156 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 157 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 158 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 159 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 160 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 161 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 162 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 163 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 164 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 165 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 166 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 167 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 168 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 169 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 170 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 171 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 172 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 173 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 174 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 175 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 176 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 177 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 178 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 179 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 180 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 181 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 182 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 183 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 184 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 185 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 186 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 187 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 188 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 189 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 190 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 191 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 192 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 193 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 194 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 195 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 196 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 197 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 198 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 199 bestvalit: 0.000000 bestmemit:    1.000000    1.000000
Iteration: 200 bestvalit: 0.000000 bestmemit:    1.000000    1.000000

***** summary of DEoptim object ***** 
best member   :  1 1 
best value    :  0 
after         :  200 generations 
fn evaluated  :  10050 times 
*************************************
Iteration: 1 bestvalit: 13.455649 bestmemit:    0.707488    0.134888
Iteration: 2 bestvalit: 13.455649 bestmemit:    0.707488    0.134888
Iteration: 3 bestvalit: 4.704277 bestmemit:   -0.716719    0.646243
Iteration: 4 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 5 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 6 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 7 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 8 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 9 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 10 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 11 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 12 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 13 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 14 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 15 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 16 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 17 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 18 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 19 bestvalit: 0.047090 bestmemit:    0.962503    0.947786
Iteration: 20 bestvalit: 0.008971 bestmemit:    1.001399    1.012270
Iteration: 21 bestvalit: 0.008971 bestmemit:    1.001399    1.012270
Iteration: 22 bestvalit: 0.008971 bestmemit:    1.001399    1.012270
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Iteration: 500 bestvalit: 0.000000 bestmemit:    1.000000    1.000000

***** summary of DEoptim object ***** 
best member   :  1 1 
best value    :  0 
after         :  500 generations 
fn evaluated  :  25050 times 
*************************************
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
Iteration: 1 bestvalit: -0.049565 bestmemit:    2.185495    2.467589
Iteration: 2 bestvalit: -0.076722 bestmemit:    2.887242    3.245730
Iteration: 3 bestvalit: -0.076722 bestmemit:    2.887242    3.245730
Iteration: 4 bestvalit: -0.076722 bestmemit:    2.887242    3.245730
Iteration: 5 bestvalit: -0.077978 bestmemit:    2.828390    3.105617
Iteration: 6 bestvalit: -0.077978 bestmemit:    2.828390    3.105617
Iteration: 7 bestvalit: -0.078753 bestmemit:    2.917866    3.118727
Iteration: 8 bestvalit: -0.079444 bestmemit:    3.057472    3.007827
Iteration: 9 bestvalit: -0.079444 bestmemit:    3.057472    3.007827
Iteration: 10 bestvalit: -0.079444 bestmemit:    3.057472    3.007827
Iteration: 11 bestvalit: -0.079444 bestmemit:    3.057472    3.007827
Iteration: 12 bestvalit: -0.079444 bestmemit:    3.057472    3.007827
Iteration: 13 bestvalit: -0.079444 bestmemit:    3.057472    3.007827
Iteration: 14 bestvalit: -0.079444 bestmemit:    3.057472    3.007827
Iteration: 15 bestvalit: -0.079444 bestmemit:    3.057472    3.007827
Iteration: 16 bestvalit: -0.079444 bestmemit:    3.057472    3.007827
Iteration: 17 bestvalit: -0.079444 bestmemit:    3.057472    3.007827
Iteration: 18 bestvalit: -0.079495 bestmemit:    2.958277    3.018335
Iteration: 19 bestvalit: -0.079495 bestmemit:    2.958277    3.018335
Iteration: 20 bestvalit: -0.079495 bestmemit:    2.958277    3.018335
Iteration: 21 bestvalit: -0.079495 bestmemit:    2.958277    3.018335
Iteration: 22 bestvalit: -0.079571 bestmemit:    2.996878    2.987374
Iteration: 23 bestvalit: -0.079571 bestmemit:    2.996878    2.987374
Iteration: 24 bestvalit: -0.079571 bestmemit:    2.996878    2.987374
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Iteration: 27 bestvalit: -0.079576 bestmemit:    3.005906    2.996582
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Iteration: 33 bestvalit: -0.079576 bestmemit:    3.005906    2.996582
Iteration: 34 bestvalit: -0.079577 bestmemit:    2.998518    2.996579
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Iteration: 38 bestvalit: -0.079577 bestmemit:    2.998518    2.996579
Iteration: 39 bestvalit: -0.079577 bestmemit:    3.001089    3.001324
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Iteration: 44 bestvalit: -0.079577 bestmemit:    3.001089    3.001324
Iteration: 45 bestvalit: -0.079577 bestmemit:    3.001089    3.001324
Iteration: 46 bestvalit: -0.079577 bestmemit:    3.000851    2.998534
Iteration: 47 bestvalit: -0.079577 bestmemit:    3.000851    2.998534
Iteration: 48 bestvalit: -0.079577 bestmemit:    2.999119    3.000263
Iteration: 49 bestvalit: -0.079577 bestmemit:    2.999819    2.999172
Iteration: 50 bestvalit: -0.079577 bestmemit:    2.999698    2.999278
Iteration: 51 bestvalit: -0.079577 bestmemit:    3.000093    3.000640
Iteration: 52 bestvalit: -0.079577 bestmemit:    3.000093    3.000640
Iteration: 53 bestvalit: -0.079577 bestmemit:    2.999867    3.000088
Iteration: 54 bestvalit: -0.079577 bestmemit:    2.999867    3.000088
Iteration: 55 bestvalit: -0.079577 bestmemit:    2.999867    3.000088
Iteration: 56 bestvalit: -0.079577 bestmemit:    2.999867    3.000088
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Iteration: 58 bestvalit: -0.079577 bestmemit:    2.999867    3.000088
Iteration: 59 bestvalit: -0.079577 bestmemit:    2.999867    3.000088
Iteration: 60 bestvalit: -0.079577 bestmemit:    2.999867    3.000088
Iteration: 61 bestvalit: -0.079577 bestmemit:    2.999867    2.999935
Iteration: 62 bestvalit: -0.079577 bestmemit:    2.999867    2.999935
Iteration: 63 bestvalit: -0.079577 bestmemit:    3.000005    2.999883
Iteration: 64 bestvalit: -0.079577 bestmemit:    3.000005    2.999883
Iteration: 65 bestvalit: -0.079577 bestmemit:    3.000024    3.000113
Iteration: 66 bestvalit: -0.079577 bestmemit:    3.000024    2.999944
Iteration: 67 bestvalit: -0.079577 bestmemit:    2.999992    2.999952
Iteration: 68 bestvalit: -0.079577 bestmemit:    2.999992    2.999952
Iteration: 69 bestvalit: -0.079577 bestmemit:    2.999988    2.999965
Iteration: 70 bestvalit: -0.079577 bestmemit:    2.999988    2.999965
Iteration: 71 bestvalit: -0.079577 bestmemit:    2.999988    2.999965
Iteration: 72 bestvalit: -0.079577 bestmemit:    2.999988    2.999965
Iteration: 73 bestvalit: -0.079577 bestmemit:    3.000009    2.999999
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Iteration: 75 bestvalit: -0.079577 bestmemit:    3.000009    2.999999
Iteration: 76 bestvalit: -0.079577 bestmemit:    3.000009    2.999999
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Iteration: 78 bestvalit: -0.079577 bestmemit:    3.000009    2.999999
Iteration: 79 bestvalit: -0.079577 bestmemit:    3.000009    2.999999
Iteration: 80 bestvalit: -0.079577 bestmemit:    3.000009    2.999999
Iteration: 81 bestvalit: -0.079577 bestmemit:    3.000006    2.999996
Iteration: 82 bestvalit: -0.079577 bestmemit:    3.000006    2.999996
Iteration: 83 bestvalit: -0.079577 bestmemit:    2.999995    2.999997
Iteration: 84 bestvalit: -0.079577 bestmemit:    2.999995    2.999997
Iteration: 85 bestvalit: -0.079577 bestmemit:    2.999999    3.000003
Iteration: 86 bestvalit: -0.079577 bestmemit:    2.999999    3.000003
Iteration: 87 bestvalit: -0.079577 bestmemit:    2.999999    3.000003
Iteration: 88 bestvalit: -0.079577 bestmemit:    2.999999    3.000003
Iteration: 89 bestvalit: -0.079577 bestmemit:    3.000002    2.999997
Iteration: 90 bestvalit: -0.079577 bestmemit:    3.000000    2.999997
Iteration: 91 bestvalit: -0.079577 bestmemit:    3.000000    2.999997
Iteration: 92 bestvalit: -0.079577 bestmemit:    3.000000    2.999997
Iteration: 93 bestvalit: -0.079577 bestmemit:    2.999999    2.999998
Iteration: 94 bestvalit: -0.079577 bestmemit:    2.999999    3.000000
Iteration: 95 bestvalit: -0.079577 bestmemit:    2.999999    3.000000
Iteration: 96 bestvalit: -0.079577 bestmemit:    2.999999    3.000000
Iteration: 97 bestvalit: -0.079577 bestmemit:    2.999999    3.000000
Iteration: 98 bestvalit: -0.079577 bestmemit:    3.000000    3.000000
Iteration: 99 bestvalit: -0.079577 bestmemit:    3.000000    3.000000
Iteration: 100 bestvalit: -0.079577 bestmemit:    3.000000    3.000000
dev.new(): using pdf(file="Rplots3.pdf")
dev.new(): using pdf(file="Rplots4.pdf")

RcppDE documentation built on May 2, 2019, 5 p.m.

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